Deep learning using tensorflow/keras
Deep learning using tensorflow/keras
Posts by Tag
- conservation planning 2
- private land conservation 2
- ecological vegetation 2
- R 2
- human-wildlife conflict 1
- leopard urban 1
- biodiversity 1
- gaussian processes 1
- geostatistics 1
- meuse 1
- gpboost 1
- spatial prediction 1
- conservation science 1
- ecology 1
- spatial nested 1
- INLA 1
- point process models 1
- forest fire 1
- log-Gaussian Cox process 1
- webscraping 1
- earth engine 1
- geospatial 1
- deep learning 1
- linux NVIDIA drivers 1
- Errors were encountered while processing 1
- R markdown 1
- state space models 1
- bayesian time series 1
- ecological forecasting 1
- JAGS 1
- kernel density estimation 1
conservation planning
Back to Top ↑private land conservation
Deep learning using tensorflow/keras
Deep learning using tensorflow/keras
ecological vegetation
Deep learning using tensorflow/keras
Deep learning using tensorflow/keras
R
Kernel Density Estimation
Benefits of using kernel smoothing:
Web scraping in R
Extract data from NSW Species Refugia website
human-wildlife conflict
Evaluating spatio-temporal trends of leopard-human conflict in Kathmandu
Photo by Magda Ehlers from Pexels
leopard urban
Evaluating spatio-temporal trends of leopard-human conflict in Kathmandu
Photo by Magda Ehlers from Pexels
biodiversity
Evaluating spatio-temporal trends of leopard-human conflict in Kathmandu
Photo by Magda Ehlers from Pexels
gaussian processes
Predicting zinc concentration in meuse using Gaussian Processes using GPBoost in R
Source: photo by Luis Miguel Justino under CC BY-NC-ND 2.0 license
geostatistics
Predicting zinc concentration in meuse using Gaussian Processes using GPBoost in R
Source: photo by Luis Miguel Justino under CC BY-NC-ND 2.0 license
meuse
Predicting zinc concentration in meuse using Gaussian Processes using GPBoost in R
Source: photo by Luis Miguel Justino under CC BY-NC-ND 2.0 license
gpboost
Predicting zinc concentration in meuse using Gaussian Processes using GPBoost in R
Source: photo by Luis Miguel Justino under CC BY-NC-ND 2.0 license
spatial prediction
You should use spatial nested cross-validation
Back to the basics
conservation science
You should use spatial nested cross-validation
Back to the basics
ecology
You should use spatial nested cross-validation
Back to the basics
spatial nested
You should use spatial nested cross-validation
Back to the basics
INLA
Point process modelling of forest fires using inlabru
Bushfires below Stacks Bluff, Tasmania, Australia photo by Matt Palmer
point process models
Point process modelling of forest fires using inlabru
Bushfires below Stacks Bluff, Tasmania, Australia photo by Matt Palmer
forest fire
Point process modelling of forest fires using inlabru
Bushfires below Stacks Bluff, Tasmania, Australia photo by Matt Palmer
log-Gaussian Cox process
Point process modelling of forest fires using inlabru
Bushfires below Stacks Bluff, Tasmania, Australia photo by Matt Palmer
webscraping
Web scraping in R
Extract data from NSW Species Refugia website
earth engine
Learning notes on Earth Engine + CNN + TensorFlow + Google Cloud
I have created these notes to document my own learning in integrating GEE + TensorFlow + Vertex AI.
geospatial
Learning notes on Earth Engine + CNN + TensorFlow + Google Cloud
I have created these notes to document my own learning in integrating GEE + TensorFlow + Vertex AI.
deep learning
Learning notes on Earth Engine + CNN + TensorFlow + Google Cloud
I have created these notes to document my own learning in integrating GEE + TensorFlow + Vertex AI.
linux NVIDIA drivers
Back to Top ↑Errors were encountered while processing
Back to Top ↑R markdown
Back to Top ↑state space models
Back to Top ↑bayesian time series
Back to Top ↑ecological forecasting
Back to Top ↑JAGS
Back to Top ↑kernel density estimation
Kernel Density Estimation
Benefits of using kernel smoothing: